High-Throughput Large Volume SEM Workflow using Sparse Scanning and In-painting Algorithms Inspired by Compressive Sensing

Abstract

We are presenting a new extension to our Cell and Tissue/Neurobiology large volume imaging workflow, with the goal of increasing acquisition speed by more than five times. Instead of scanning dense square-grid frames, in the conventional way, our approach is here to explore the use of sparse scanning and inpainting techniques inspired by Compressive Sensing (CS) Sparse samples are obtained by pseudo-random scan patterns, and reconstruction algorithms are used to recover the dense volume data. The goal is to recover 3D datasets with minimum loss of information. Techniques inspired by CS gained wide attention over the last decade and are now being used in various applications where sensor bandwidth is a limiting factor. They have been recently explored for SEM and STEM applications [2][3]. In the context of nano-scale cell biology volume acquisition, we expect these techniques to ultimately increase the imaging throughput by nearly an order of magnitude. We will discuss additional advantages of this approach, such as the low-dose imaging of sensitive specimens, and the good compatibility with backscatter electron imaging. A key enabler of any sparse scan application to EM is the accurate control of scan locations. It has been shown in We have developed advanced minimum-path scanning strategies to address this issue. The scanning technique is illustrated in Fig. The right two images of Fig. In future work we will compare pseudo-random sparse sampling, in combination with a reconstruction algorithm based on CS-inspired in-painting, to conventional grid sampling of the same effective dose, in combination with a de-noising algorithm, also based on CS. CS machine learning algorithms build patch-dictionaries, which are used as the building blocks for data representation During live acquisition runs, such dictionaries can be used to in-paint with high fidelity, the sparsely sampled datasets (Figure We are implementing the new sparse scanning modules on SEM platforms, which also employ the Multi Energy Deconvolution SEM (MED-SEM) technology and Serial Block Face (SBF) imaging By incorporating CS, we will have an instrument allowing for both high-resolution isotropic imaging, and the fast acquisition of very large datasets (Figure